# Computations
import numpy as np
import pandas as pd
import pickle

# preprocessing
from sklearn import preprocessing
import re

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## progressbar
import progressbar

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
import matplotlib.colors as mcolors
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

Home / Business Analytics and Modeling

IBM HR Analytics

• Preprocessing
• Exploratory Data Analysis
• Modeling: Gaussian Naive-Bayes

IBM HR Analytics Employee Attrition and Performance Dataset

In this study, we analyze HR data available from kaggle.com. This data is fictional and it is created by IBM data scientists.

Categorical Parameters:

	1	2	3	4	5
Education	Below College	College	Bachelor	Master	Doctor
Environment Satisfaction	Low	Medium	High	Very High
Job Involvement	Low	Medium	High	Very High
Job Satisfaction	Low	Medium	High	Very High
Performance Rating	Low	Good	Excellent	Outstanding
Relationship Satisfaction	Low	Medium	High	Very High
WorkLife Balance	Bad	Good	Better	Best

This can be encoded as follows,

Categorical_Dict = {'Education': {1:'Below College', 2:'College',3:'Bachelor', 4: 'Master', 5:'Doctor'},
                    'Environment Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Job Involvement': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Job Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Performance Rating': {1:'Low', 2:'Good', 3:'Excellent', 4:'Outstanding'},
                    'Relationship Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Work Life Balance': {1:'Bad', 2:'Good', 3:'Better', 4:'Best'}}

Loading the Dataset

Path = 'Data/WA_Fn-UseC_-HR-Employee-Attrition.xlsx'
Data = pd.read_excel(Path)
Temp = [re.sub(r"(\w)([A-Z])", r"\1 \2", x) for x in Data.columns]
Temp = [x.replace(' Curr ', ' Current ').replace('18',' 18').replace('Num ','Number Of ') for x in Temp]
Data.columns = Temp
del Temp
Data['Business Travel'] = Data['Business Travel'].str.replace('_',' ')
display(Data.head(8).style.hide_index())
Target = 'Attrition'
Featured_Columns = list(set(Data.columns) - {Target, 'Employee Number'})

Age	Attrition	Business Travel	Daily Rate	Department	Distance From Home	Education	Education Field	Employee Count	Employee Number	Environment Satisfaction	Gender	Hourly Rate	Job Involvement	Job Level	Job Role	Job Satisfaction	Marital Status	Monthly Income	Monthly Rate	Number Of Companies Worked	Over 18	Over Time	Percent Salary Hike	Performance Rating	Relationship Satisfaction	Standard Hours	Stock Option Level	Total Working Years	Training Times Last Year	Work Life Balance	Years At Company	Years In Current Role	Years Since Last Promotion	Years With Current Manager
41	Yes	Travel Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
49	No	Travel Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
37	Yes	Travel Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
33	No	Travel Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
27	No	Travel Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2
32	No	Travel Frequently	1005	Research & Development	2	2	Life Sciences	1	8	4	Male	79	3	1	Laboratory Technician	4	Single	3068	11864	0	Y	No	13	3	3	80	0	8	2	2	7	7	3	6
59	No	Travel Rarely	1324	Research & Development	3	3	Medical	1	10	3	Female	81	4	1	Laboratory Technician	1	Married	2670	9964	4	Y	Yes	20	4	1	80	3	12	3	2	1	0	0	0
30	No	Travel Rarely	1358	Research & Development	24	1	Life Sciences	1	11	4	Male	67	3	1	Laboratory Technician	3	Divorced	2693	13335	1	Y	No	22	4	2	80	1	1	2	3	1	0	0	0

First off, let's take a look at the dataset

def Data_Plot(Inp, Title = None, W = None):
    data_info = Inp.dtypes.astype(str).to_frame(name='Data Type')
    Temp = Inp.isnull().sum().to_frame(name = 'Number of NaN Values')
    data_info = data_info.join(Temp, how='outer')
    data_info ['Size'] = Inp.shape[0]
    data_info['Percentage'] = 100 - np.round(100*(data_info['Number of NaN Values']/Inp.shape[0]),2)
    data_info = data_info.reset_index(drop = False).rename(columns = {'index':'Features'})
    #
    fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type',
                 text = 'Percentage',
                 color_discrete_sequence = ['PaleGreen', 'LightCyan', 'PeachPuff', 'Pink', 'Plum'],
                 hover_data = data_info.columns)
    fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1.01, y=.5, traceorder="normal",
                                                         bordercolor="DarkGray", borderwidth=1))
    if not W == None:
        fig.update_layout(width = W)
    fig.update_traces(texttemplate= 10*' ' + '%%{text}', textposition='inside')
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
    if not Title == None:
        fig.update_layout(title={'text': '<b>' + Title + '<b>', 'x':0.5,
                                         'y':0.90, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()
    return data_info

_ = Data_Plot(Data, Title = 'IBM HR Analytics Employee Attrition and Performance Dataset')

Moreover,

def Distinct_Observations(Inp, Target = Target, Featured_Columns = None, YL = None):
    if Featured_Columns == None:
        Featured_Columns = list(set(Data.columns) - {Target})
    Temp = Inp[Featured_Columns].nunique()
    fig = go.Figure([go.Bar(x=Temp.index, y=Temp.values)])
    fig.update_traces(marker_line_color= 'Navy', marker_line_width=1, opacity=1, showlegend = False)
    fig.update_layout(legend_orientation='v', plot_bgcolor= 'white', height= 450, width= 980,
                     title={'text': '<b>' + 'Distinct Observations in Each Column' + '<b>', 'x':0.5,
                            'y': 0.92, 'xanchor': 'center', 'yanchor': 'top'},
                     yaxis_title='Frequency')
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                         zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                         showgrid=False, gridwidth=1, gridcolor='Lightgray')
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=True, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray')
    if not YL == None:
        fig.update_yaxes(range =[0, YL])
    fig.show()
    return Temp
_ = Distinct_Observations(Inp = Data, Featured_Columns = Featured_Columns, YL = 1500)

Preprocessing

We also need to convert categorical data to numeric data.

def dtypes_group(Inp):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Features','Count'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Features'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
        Out.loc[Out.index == c, 'Count'] = len(Temp.loc[Temp['Data Type'] == c].index.tolist())
    Out = Out.reset_index(drop = False).rename(columns = {'index':'Data Type'})
    Out['Data Type'] = Out['Data Type'].astype(str)
    return Out

def dtype_sep(Inp):
    Temp = Inp.dtypes.reset_index(drop = False)
    Temp.columns = ['Features', 'Data Type']
    Temp['Data Type'] = Temp['Data Type'].astype(str)
    # Numeric_Columns
    Numeric_Columns = Temp.loc[Temp['Data Type'].isin(['int64', 'int32', 'float64', 'float32']),'Features'].tolist()
    # Categorical_Columns
    Categorical_Columns = Temp.loc[Temp['Data Type'] == 'object','Features'].tolist()
    return Numeric_Columns, Categorical_Columns

Numeric_Columns, Categorical_Columns = dtype_sep(Data)
display(dtypes_group(Data).style.hide_index())

Data Type	Features	Count
int64	['Age', 'Years In Current Role', 'Years At Company', 'Work Life Balance', 'Training Times Last Year', 'Total Working Years', 'Stock Option Level', 'Standard Hours', 'Relationship Satisfaction', 'Performance Rating', 'Percent Salary Hike', 'Number Of Companies Worked', 'Monthly Rate', 'Monthly Income', 'Years Since Last Promotion', 'Job Satisfaction', 'Years With Current Manager', 'Job Level', 'Daily Rate', 'Distance From Home', 'Education', 'Employee Number', 'Environment Satisfaction', 'Employee Count', 'Hourly Rate', 'Job Involvement']	26
object	['Over 18', 'Job Role', 'Education Field', 'Gender', 'Department', 'Business Travel', 'Attrition', 'Over Time', 'Marital Status']	9

We can use LabelEncoder for converting categorical to numeric using. Therefore,

# A copy of the dataset
df = Data.copy()

N = len(Categorical_Columns)
# Progressbar
Counter = 0
Progress_Bar = progressbar.ProgressBar(maxval= N, widgets=[progressbar.Bar('=', '|', '|'), progressbar.Percentage()])

#--------------- the loop ----------------------
Progress_Bar.start()
for i in range(N):
    le = preprocessing.LabelEncoder()
    le.fit(list(df[Categorical_Columns[i]]))
    df[Categorical_Columns[i]] = le.transform(df[Categorical_Columns[i]])
    del le
    Progress_Bar.update(Counter)
    Counter+=1
Progress_Bar.finish()
#--------------- End of the loop ---------------

# Finally, converting values of df back to integers.
df = df.astype(int)

display(dtypes_group(df).style.hide_index())

|=========================================================================|100%

Data Type	Features	Count
int32	['Age', 'Monthly Rate', 'Number Of Companies Worked', 'Over 18', 'Over Time', 'Percent Salary Hike', 'Performance Rating', 'Monthly Income', 'Relationship Satisfaction', 'Stock Option Level', 'Total Working Years', 'Training Times Last Year', 'Work Life Balance', 'Years At Company', 'Years In Current Role', 'Standard Hours', 'Years Since Last Promotion', 'Marital Status', 'Job Role', 'Attrition', 'Business Travel', 'Daily Rate', 'Department', 'Distance From Home', 'Education', 'Job Satisfaction', 'Education Field', 'Employee Number', 'Environment Satisfaction', 'Gender', 'Hourly Rate', 'Job Involvement', 'Job Level', 'Employee Count', 'Years With Current Manager']	35

df.to_csv (Path.split(".")[0]+'.csv', index = None, header=True)

Moreover,

def FeatBins(Inp, Bins, replace = True):
    Bins = [int(x) for x in Bins]
    Out = pd.cut(Inp, bins = pd.IntervalIndex.from_tuples([(x, y) for x, y in zip(Bins[:-1],Bins[1:])]))
    Temp = np.sort(Out.astype('str').unique())
    Dict = dict(zip(Temp, np.arange(len(Temp))))
    if replace:
        Out = Out.astype('str').replace(Dict)
    else:
        Out = Out.astype('str')
        try:
            Out = Out.str.replace(pat = '(', repl = '[').str.replace(pat = '-1', repl = '0')
        except:
            pass
    return Out

Bin_Dict = {'Age': [15, 25, 35, 50, 60, 80],
            'Daily Rate': [100, 300, 600, 1000, 1500],
            'Distance From Home': [0, 5, 10, 20, 30],
            'Hourly Rate': [25, 50, 75, 101],
            'Monthly Income': [1e3, 3e3, 7e3, 1e4, 2e4],
            'Monthly Rate': [2e3, 1e4, 2e4, 3e4],
            'Number Of Companies Worked': [-1, 2, 4, 6, 10],
            'Percent Salary Hike': list(np.arange(10, 27, 4)),
            'Total Working Years': [-1, 10, 20, 30, 41],
            'Years At Company': [-1, 10, 20, 30, 41],
            'Years In Current Role': [-1, 4, 8, 12, 19],
            'Years Since Last Promotion': [-1, 5, 10, 16],
            'Years With Current Manager': [-1, 4, 8, 12, 18]}

for Feat in Bin_Dict.keys():
    df[Feat] = FeatBins(Inp = df[Feat], Bins = Bin_Dict[Feat])

_ = Distinct_Observations(Inp = df, Featured_Columns = Featured_Columns, YL = 10)

Variance of the Features

Features with variance zero

First, we remove features that have zero variance as these features don't add anything to our modeling.

# var
Temp = df[Featured_Columns].var().sort_values(ascending = False)
Temp = Temp.loc[Temp.round(16) ==0].index.tolist()
print(Back.BLACK + Fore.CYAN + Style.NORMAL + 'Features with variance zero' + Style.RESET_ALL + ':' + '%s' % ', '.join(Temp))
df = df.drop(columns = Temp)
del Temp

Features with variance zero:Employee Count, Over 18, Standard Hours

Features with high variance

Moreover, high variance for some features can hurt our modeling process. For this reason, we would like to standardize features by removing the mean and scaling to unit variance. In this article, we demonstrated the benefits of scaling data using StandardScaler().

Aditional_Columns = [Target, 'Employee Number']
X = df.drop(columns = Aditional_Columns)

def Feature_Normalize(X, PD):
    def List_Break(mylist, n = PD['word_break']):
        Out = []
        for x in mylist:
            y = x.split()
            if len(y)> n:
                z = ' '.join(y[:n])
                sep = np.arange(0, len(y), n)[1:]
                for n in sep:
                    z = z + '\n'+ ' '.join(y[n:])
            else:
                z = ' '.join(y)
            Out.append(z)
        return Out

    scaler = preprocessing.StandardScaler()
    X_std = scaler.fit_transform(X)
    X_std = pd.DataFrame(data = X_std, columns = X.columns)

    fig, ax = plt.subplots(2, 1, figsize = PD['figsize'])
    ax = ax.ravel()
    CP = [sns.color_palette("OrRd", 20), sns.color_palette("Greens", X.shape[1])]
    Names = ['Variance of the Features', 'Variance of the Features (Standardized)']
    Sets = [X, X_std]
    kws = dict(label='Feature\nVariance', aspect=10, shrink= .3)
    for i in range(len(ax)):
        Temp = Sets[i].var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T
        _ = sns.heatmap(Temp, ax=ax[i], annot=True, square=True,  cmap = CP[i],
                        linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0],  annot_kws={"size": PD['annot_text_size']},
                        cbar_kws=kws)
        if not PD['word_break'] == None:
            mylist = List_Break(Temp.T.index.tolist())
            _ = ax[i].xaxis.set_ticklabels(mylist)
        _ = ax[i].set_yticklabels('')
        _ = ax[i].set_title(Names[i], weight='bold', fontsize = 14)
        _ = ax[i].set_aspect(1)
        del Temp
    plt.subplots_adjust(hspace=PD['hspace'])
    Out = pd.DataFrame(data = X_std, columns = X.columns.tolist())
    return Out

PD = dict(figsize = (20, 7), hspace = 0.2, annot_text_size = 10, word_break = 2)
df[X.columns.tolist()] = Feature_Normalize(X, PD)

Feature Correlation

def Feature_Corr(df, PD, Target = Target):
    def List_Break(mylist, n = PD['word_break']):
        Out = []
        for x in mylist:
            y = x.split()
            if len(y)> n:
                z = ' '.join(y[:n])
                sep = np.arange(0, len(y), n)[1:]
                for n in sep:
                    z = z + '\n'+ ' '.join(y[n:])
            else:
                z = ' '.join(y)
            Out.append(z)
        return Out
    fig, ax = plt.subplots(1, 1, figsize = PD['figsize'])
    CM = df.corr().round(2)
    CM = CM.loc[(CM.index == Target)].drop(columns = Target).T.sort_values(by = Target).T
    kws = dict(label='Feature\nVariance', aspect=10, shrink= .3)
    _ = sns.heatmap(CM, ax=ax, annot=True, square=True,  cmap = PD['Colormap'], linewidths = 0.8, vmin=0,
                    vmax=CM.max(axis =1)[0],  annot_kws={"size": PD['annot_text_size']}, cbar_kws=kws)
    if not PD['word_break'] == None:
        mylist = List_Break(CM.T.index.tolist())
        _ = ax.xaxis.set_ticklabels(mylist)
    _ = ax.set_yticklabels('')
    _ = ax.set_aspect(1)

PD.update(Colormap = 'Greens')
Feature_Corr(df, PD)

Saving

df.to_csv (Path.split(".")[0]+'_STD.csv', index = None, header=True)

with open(Path.split(".")[0] + '_Categorical_Dict.pkl', 'wb') as fp:
    pickle.dump(Categorical_Dict, fp, protocol=pickle.HIGHEST_PROTOCOL)
    
with open(Path.split(".")[0] + '_Bin_Dict.pkl', 'wb') as fp:
    pickle.dump(Bin_Dict, fp, protocol=pickle.HIGHEST_PROTOCOL)

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References

1. Kaggle Dataset: IBM HR Analytics Employee Attrition & Performance

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